Overview
Background
My research interests centre on using quantitative genetics to drive genetic gain and efficiency in plant and animal breeding programmes.
Previous work in the UK focused on using genomic information prediction to demonstrate and exploit synergies between plant and animal breeding. Stochastic simulations were used to quantify the impact of new genomic breeding strategies in a wide variety of settings; from low to middle-income (LMIC) dairy cattle breeding programs to large, well-funded maize breeding programs.
My work at QAAFI and the ARC Centre of Excellence for Plant Success in Nature & Agriculture focuses on the development of prediction methods that combine biological, environmental and management information under a unifying framework, to enhance our ability to identify breeding parents, varieties and genotype-by-agronomic management (GxM) solutions that are best suited for future climates.
Availability
- Dr Owen Powell is:
- Available for supervision
- Media expert
Qualifications
- Bachelor (Honours) of Development Biology, University of Edinburgh
- Masters (Research) of Science, University of Edinburgh
- Doctor of Philosophy, University of Edinburgh
Research impacts
Dr Powell helps public and private genetic improvement programs to find better ways to predict the outcomes of selective breeding.
His core work focuses on developing, applying and optimising prediction methods to accelerate rates of sustainable genetic improvement.
Dr Powell is involved in the research and HDR student supervision on projects that span plant, animal and aquaculture species.
Supervision
Availability
- Dr Owen Powell is:
- Available for supervision
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Supervision history
Current supervision
-
Doctor Philosophy
Predicting Plant Success For Future Generations
Principal Advisor
Other advisors: Professor Mark Cooper
-
Doctor Philosophy
Optimising training populations for enhanced prediction
Principal Advisor
Other advisors: Professor Mark Cooper
-
Doctor Philosophy
Assessment of machine learning methods to discover novel models of gene networks to improve genomic prediction for plant breeding
Associate Advisor
Other advisors: Dr James Lefevre, Dr Melanie Wilkinson, Professor Mark Cooper
-
Doctor Philosophy
Machine learning strategies to optimize genomic prediction in polyploid sugarcane
Associate Advisor
Other advisors: Dr Eric Dinglasan, Associate Professor Elizabeth Ross, Professor Ben Hayes
-
Doctor Philosophy
Experimental investigation in Arabidopsis thaliana of realised selection trajectories for complex branching and flowering traits under the control of gene networks following application of genomic prediction methods.
Associate Advisor
Other advisors: Professor Christine Beveridge, Professor Mark Cooper
-
Doctor Philosophy
New approaches to maximise gain and genetic diversity in plant breeding programs
Associate Advisor
Other advisors: Professor Ben Hayes
-
Doctor Philosophy
Rapid cycle multi-trait genomic prediction to evaluate plant development and architecture ideotypes
Associate Advisor
Other advisors: Professor Christine Beveridge, Professor Mark Cooper
-
Doctor Philosophy
Genomic selection for finfish Breeding Programs
Associate Advisor
Other advisors: Professor Ben Hayes
-
Doctor Philosophy
APSIM-WGP for yield stability in soybean I
Associate Advisor
Other advisors: Professor Mark Cooper, Dr Melanie Wilkinson
Completed supervision
-
2023
Doctor Philosophy
Optimising Genomic Selection for Sugarcane
Associate Advisor
Other advisors: Professor Lee Hickey, Dr Kai Voss-Fels, Associate Professor Elizabeth Ross, Professor Ben Hayes
Media
Enquiries
Contact Dr Owen Powell directly for media enquiries about:
- Computational Biology
- Computer Simulations
- Data Science
- Genetics
- Plant Breeding
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